big update since I first posted this. Engram went from ~2,300 lines to 6,200+. Main changes: Replaced the flat 30-day exponential decay with FSRS-6 (same spaced repetition algorithm behind modern Anki, 21 parameters, trained on hundreds of millions of reviews). Every memory access is an implicit review now — stuff agents actually use builds stability, stuff they don't fades out naturally. Power-law forgetting curve instead of exponential. Each memory tracks dual strengths (Bjork & Bjork 1992) — storage strength that accumulates and never decays, and retrieval strength that does. A memory can be deeply encoded but temporarily hard to retrieve. Recall scoring got noticeably better after this. Moved from SQLite to libsql for native vector search. Embeddings are FLOAT32(384) with an ANN index now — vector_top_k() instead of cosine similarity loops in JS. O(log n) vs O(n). Conversation storage and search — full chat logs linked to memory episodes. Useful for tracing why a memory exists. Episodic memory — group memories into sessions/episodes. Everything from before still works — local embeddings, auto-linking, versioning, dedup, contradiction detection, time-travel, reflections, graph viz, multi-tenant, MCP server, TypeScript/Python SDKs. Still docker compose up, still MIT. Demo: https://demo.engram.lol/gui